Principal Platform Engineer, AI Engineering

Remote-US

We are a healthcare technology company that provides platforms and solutions to improve the management and access of cost‑effective pharmacy benefits. Our technology helps enterprise and partnership clients simplify their businesses and helps consumers save on prescriptions.

As a leader in SaaS technology for healthcare, we offer innovative solutions with integrated intelligence on a single enterprise platform that connects the pharmacy ecosystem. With our expertise and modern, modular platform, our partners use real‑time data to transform their business performance and optimize their innovative models in the marketplace.

RxSense is a privately held health technology company that is re‑envisioning the platforms and data solutions used to manage pharmacy benefits in order to make prescription drugs more affordable for everyone. RxSense also provides prescription benefit solutions directly to millions of people through its consumer brand, Single Care. We have saved our customers over $4B on prescription medications since 2015.

We are a team of forward thinking, experienced health and technology professionals working together to solve big problems and create value in an industry that is personal for everyone - healthcare.

RxSense sits at the intersection of pharmacy benefits and technology. We are building a new cloud platform that we own end to end, and it will carry the next generation of RxSense products, from established pharmacy benefit services to AI-native applications.

We are hiring a Principal Platform Engineer to lead the technical build. You will set the direction for how services across engineering are built, deployed, secured, observed, and paid for. This is a greenfield platform with real production stakes: the decisions you make in the first year become the defaults every engineer works inside of for years after.

This is a hands‑on principal role, not an architecture‑diagram role. You will write Terraform and Helm, shape CI/CD, harden clusters, and set the standards the rest of engineering codes against.

You will be embedded with AI Engineering, the team pushing hardest on the platform today, and you will partner closely with data engineering so analytics and pipeline workloads are first‑class from the start.

• Build the infrastructure as code foundation. Design and maintain a Terraform monorepo across dev, QA, staging, and production, covering Kubernetes clusters, networking, IAM, and per‑application platform stacks. Keep state layout, module boundaries, and provider baselines clean and current.
• Run Kubernetes at production quality. Operate EKS clusters end to end: node lifecycle, autoscaling, ingress, workload identity, secrets delivery, and cluster security. Keep clusters hardened and appropriately isolated.
• Build and defend the deploy pipeline. Build push‑based CI/CD on self‑hosted Git Hub Actions runners, with build‑once, promote‑everywhere artifact immutability across environments. Enforce a promotion flow so no environment is ever skipped and production always mirrors a released artifact.
• Make the platform the fastest path to production. Maintain a shared Helm chart library and per‑service charts (backend, frontend, scheduled jobs) that every service deploys through. Build golden paths so a new service reaches production on day one with logging, metrics, secrets, identity, and a pipeline already wired in. Push per‑application behavior into configuration rather than chart branching.
• Harden the security and compliance posture. Set least‑privilege IAM, secrets management, network boundaries, image provenance, and production guardrails. Make controls automatic where you can and auditable where you cannot, so evidence for security reviews falls out of the platform instead of getting assembled by hand.
• Keep cloud spend predictable. Treat cost as a platform property. Establish tagging and allocation that answer what each service and environment actually costs, right‑size compute, and keep spend predictable as traffic, data, and model inference grow.
• Build observability in, not on. Establish structured logging, metrics, tracing, and…